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Agile Multiscale Modelling of the Thermo-Mechanical Processing of an Aluminium Alloy

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Abstract:

The multiscale modelling of the behaviour of metal alloys during processing is often limited by the computing power required to run them. The Agile Multiscale Methodology was conceived to enhance the designing and controlling of complex multiscale models through an automatic run-time adaptation of its constitutive sub-models. This methodology is used to simulate the behaviour of an 6082 aluminium alloy during its thermomechanical treatment. The macroscopic deformation, the work-hardening and the state of precipitation are computed in different modules, allowing the coupling of several software solutions (DEFORMTM2D and © MatCalc) through an external storage of the relevant data.

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Periodical:

Key Engineering Materials (Volumes 651-653)

Pages:

1319-1324

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.651-653.1319

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Online since:

July 2015

Authors:

Piotr Macioł*, Romain Bureau, Maria Cecilia Poletti, Christof Sommitsch, Piotr Warczok, Ernst Kozeschnik

Keywords:

Multiscale Modelling, Numerical Models, Precipitation, Thermo-Mechanical Treatment

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] O.M. Steinhauser, Computational Multiscale Modeling of Fluids and Solids, Springer Verlag, (2008).

Google Scholar

[2] W. E, Principles of Multiscale Modeling, Cambridge University Press, (2011).

Google Scholar

[3] P. Macioł, L. Gotfryd, A. Macioł, Knowledge based system for runtime controlling of multiscale model of ion-exchange solvent extraction, in: AIP Conference Proceedings, ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics, American Institute of Physics, Kos, Greece, (2012).

DOI: 10.1063/1.4756078

Google Scholar

[4] P. Macioł, A. Krumphals, S. Jędrusik, A. Macioł, C. Sommitsch, Rule-based expert system application to optimizing of multiscale model of hot forging and heat treatment of Ti-6Al-4V, in: V International Conference on Computational Methods for Coupled Problems in Science and Engineering COUPLED PROBLEMS 2013, Ibiza, (2013).

Google Scholar

[5] P. Macioł, R. Bureau, C. Sommitsch, An Object-Oriented Analysis of Complex Numerical Models, in: Key Engineering Materials, (2014) 1356–1363.

DOI: 10.4028/www.scientific.net/kem.611-612.1356

Google Scholar

[6] J.R. Terkildsen, S. Niederer, E.J. Crampin, P. Hunter, N.P. Smith, Using Physiome standards to couple cellular functions for rat cardiac excitation-contraction., Experimental Physiology. 93 (2008) 919–29.

DOI: 10.1113/expphysiol.2007.041871

Google Scholar

[7] J. Cooper, A. Corrias, D. Gavaghan, D. Noble, Considerations for the use of cellular electrophysiology models within cardiac tissue simulations., Progress in Biophysics and Molecular Biology. 107 (2011) 74–80.

DOI: 10.1016/j.pbiomolbio.2011.06.002

Google Scholar

[8] H. Keitel, G. Karaki, T. Lahmer, S. Nikulla, V. Zabel, Evaluation of coupled partial models in structural engineering using graph theory and sensitivity analysis, Engineering Structures. 33 (2011) 3726–3736.

DOI: 10.1016/j.engstruct.2011.08.009

Google Scholar

[9] J. Borgdorff, M. Mamonski, B. Bosak, K. Kurowski, M. Ben Belgacem, B. Chopard, et al., Distributed multiscale computing with MUSCLE 2, the Multiscale Coupling Library and Environment, Journal of Computational Science. 5 (2014) 719–731.

DOI: 10.1016/j.jocs.2014.04.004

Google Scholar

[10] U.F. Kocks, Laws for Work-Hardening and Low-Temperature Creep, Journal of Engineering Materials and Technology. 98 (1976) 76.

DOI: 10.1115/1.3443340

Google Scholar

[11] E. Nes, Modelling of work hardening and stress saturation in FCC metals, Progress in Materials Science. 41 (1997) 129–193.

DOI: 10.1016/s0079-6425(97)00032-7

Google Scholar

[12] F. Roters, D. Raabe, G. Gottstein, Work hardening in heterogeneous alloys—a microstructural approach based on three internal state variables, Acta Materialia. 48 (2000) 4181–4189.

DOI: 10.1016/s1359-6454(00)00289-5

Google Scholar

[13] D. Hull, D.J. Bacon, Introduction to Dislocations, 4th ed., Butterworth-Heinemann, (2001).

Google Scholar

[14] Y. Estrin, H. Mecking, A unified phenomenological description of work hardening and creep based on one-parameter models, Acta Metallurgica. 32 (1984) 57–70.

DOI: 10.1016/0001-6160(84)90202-5

Google Scholar

[15] E. Kabliman, P. Sherstnev, Integrated Modeling of Strength Evolution in Al-Mg-Si Alloys during Hot Deformation, in: Materials Science Forum, (2013) 429–433.

DOI: 10.4028/www.scientific.net/msf.765.429

Google Scholar